Automobile Monitoring Systems and Methods for Detecting Damage and Other Conditions

ABSTRACT

A method of determining damage to property includes inputting historical data into a machine learning model to identify an insured type, features, and/or characteristics. The method may include identifying a peril, repair and/or replacement cost of the vehicle by analyzing a digital image from a device of an insured, the digital image depicting damage to the vehicle, The method may include inputting the digital image into the trained machine learning model to identify a type, feature, and/or characteristic of the vehicle, and may include identifying a peril, repair, and/or replacement cost associated with the vehicle. A method may include receiving and/or retrieving free-form text associated with an insurance claim and/or a vehicle, identifying at least one key word composing the free-form text, and determining based on the at least one key word a cause of loss and/or peril that caused damage to the vehicle.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to and the benefit of:

-   -   U.S. application Ser. No. 62/564,055, filed Sep. 27, 2017 and        entitled “REAL PROPERTY MONITORING SYSTEMS AND METHODS FOR        DETECTING DAMAGE AND OTHER CONDITIONS;”    -   U.S. application Ser. No. 62/580,655, filed Nov. 2, 2017 and        entitled REAL PROPERTY MONITORING SYSTEMS AND METHODS FOR        DETECTING DAMAGE AND OTHER CONDITIONS;”    -   U.S. application Ser. No. 62/610,599, filed Dec. 27, 2017 and        entitled “AUTOMOBILE MONITORING SYSTEMS AND METHODS FOR        DETECTING DAMAGE AND OTHER CONDITIONS;”

U.S. application Ser. No. 62/621,218, filed Jan. 24, 2018 and entitled“AUTOMOBILE MONITORING SYSTEMS AND METHODS FOR LOSS MITIGATION ANDCLAIMS HANDLING;”

U.S. application Ser. No. 62/621,797, filed Jan. 25, 2018 and entitled“AUTOMOBILE MONITORING SYSTEMS AND METHODS FOR LOSS RESERVING ANDFINANCIAL REPORTING;”

U.S. application Ser. No. 62/580,713, filed Nov. 2, 2017 and entitled“REAL PROPERTY MONITORING SYSTEMS AND METHODS FOR DETECTING DAMAGE ANDOTHER CONDITIONS;”

-   -   U.S. application Ser. No. 62/618,192, filed Jan. 17, 2018 and        entitled “REAL PROPERTY MONITORING SYSTEMS AND METHODS FOR        DETECTING DAMAGE AND OTHER CONDITIONS;”    -   U.S. application Ser. No. 62/625,140, filed Feb. 1, 2018 and        entitled “SYSTEMS AND METHODS FOR ESTABLISHING LOSS RESERVES FOR        BUILDING/REAL PROPERTY INSURANCE;”

U.S. application Ser. No. 62/646,729, filed Mar. 22, 2018 and entitled“REAL PROPERTY MONITORING SYSTEMS AND METHODS FOR LOSS MITIGATION ANDCLAIMS HANDLING;”

-   -   U.S. application Ser. No. 62/646,735, filed Mar. 22, 2018 and        entitled “REAL PROPERTY MONITORING SYSTEMS AND METHODS FOR RISK        DETERMINATION,”— U.S. application Ser. No. 62/646,740, filed        Mar. 22, 2018 and entitled “SYSTEMS AND METHODS FOR ESTABLISHING        LOSS RESERVES FOR BUILDING/REAL PROPERTY INSURANCE;”    -   U.S. application Ser. No. 62/617,851, filed Jan. 16, 2018 and        entitled. “IMPLEMENTING MACHINE LEARNING FOR LIFE AND HEALTH        INSURANCE PRICING AND UNDERWRITING;”

U.S. application Ser. No. 62/622,542, filed Jan. 26, 2018 and entitled“IMPLEMENTING MACHINE LEARNING FOR LIFE AND HEALTH INSURANCE LOSSMITIGATION AND CLAIMS HANDLING;”

U.S. application Ser. No. 62/632,884, filed Feb. 20, 2018 and entitled“IMPLEMENTING MACHINE LEARNING FOR LIFE AND HEALTH INSURANCE LOSSRESERVING AND FINANCIAL REPORTING;”

U.S. application Ser. No. 62/652,121, filed Apr. 3, 2018 and entitled“IMPLEMENTING MACHINE LEARNING FOR LIFE AND HEALTH INSURANCE CLAIMSHANDLING;”

the entire disclosures of which are hereby incorporated by referenceherein in their entireties.

FIELD OF INVENTION

This disclosure generally relates to detecting damage, loss, and/orother conditions associated with an automobile and human passengers,operators, and/or pedestrians to determine risk levels for insurance tobetter and/or more efficiently match price to risk.

BACKGROUND

As computer and computer networking technology has become less expensiveand more widespread, more and more devices have started to incorporatedigital “smart” functionalities. For example, controls and sensorscapable of interfacing with a network may now be incorporated intodevices such as vehicles and/or traffic control systems. Furthermore, itis possible for one or more vehicle and/or central controllers tointerface with the smart devices or sensors.

However, conventional systems may not be able to automatically detectand characterize various conditions or damage associated with a vehicleor building. Additionally, conventional systems may not be able todetect or sufficiently identify and describe damage that is hidden fromhuman view, and that typically has to be characterized by explicit humanphysical exploration, extent and range of electrical malfunctions, etc.Conventional systems further may not be able to formulate precisecharacterizations of loss without including unconscious biases, and maynot be able to equally weight all historical data in determining lossmitigation factors.

BRIEF SUMMARY

The present disclosure generally relates to systems and methods fordetecting damage, loss, and/or other conditions associated with avehicle using a computer system and/or a building, land, structure, orother real property using a property monitoring system. Embodiments ofexemplary systems and computer-implemented methods are summarized below.The methods and systems summarized below may include additional, less,or alternate components, functionality, and/or actions, including thosediscussed elsewhere herein.

In one aspect, the present embodiments may relate to determining anautomobile-based risk level via one or more processors, training aneural network to identify risk factors that are predictive electronicclaim features, receiving information corresponding to (i) anautomobile, and/or (ii) an automobile operator, analyzing theinformation using the trained neural network to generate one or morerisk indicators, determining, by analyzing the risk indicators, a risklevel corresponding to the automobile, and/or displaying, to a user, aninsurance quotation based upon analyzing the risk indicators. Theautomobile may be a smart, autonomous, or semi-autonomous vehicle, andhave sensors, software, and electronic components that direct autonomousor semi-autonomous vehicle features or technologies — each of which mayhave a various levels of risk, or lack thereof, that may be analyzed anddetermined by the present embodiments. Systems and methods mayautomatically generate risk models for various types of vehicleinsurance types and loss types, such as by the application of artificialintelligence and machine learning methods as disclosed herein, toprovide more granular risk models, leading to more accurate commercialofferings, and more appropriate matching premium price to actual risk.

In another aspect_(;) a computer-implemented method of determining anautomobile-based risk level via one or more processors may includetraining, via one or more processors, a neural network to identify riskfactors that are predictive of electronic vehicle claim records. Theneural network may include a plurality of layers, and an input layerfrom among the plurality of layers may include a plurality of inputparameters—with each corresponding to a different claim attribute. Themethod may include, via one or more processors, receiving informationcorresponding to (i) an automobile, and/or (ii) an automobile operator;and analyzing the information using the trained neural network.Analyzing the information may include generating, within the pluralityof layers, one or more risk indicators corresponding to the information.The method may also include determining a risk level corresponding tothe vehicle. The method may include additional, less, or alternateactions, including those discussed elsewhere herein.

In another aspect, a computing system may include one or moreprocessors, and one or more memories storing instructions. When theinstructions are executed by the one or more processors, they may causethe computing system to provide a first application to a user of aclient computing device. The first application, when executing on theclient computing device, may cause the client computing device to obtaina set of information from an input device of the client computingdevice, and transmit, via a communication network interface of theclient computing device, the set of information to a remote computingsystem. The instructions may cause the computing system to receive, atthe remote computing system, the set of information and process, at theremote computing system, the set of information. The instructions maycause the computing system to identify, by the remote computing system,one or more risk indications, at least in part, by applying the set ofinformation to a trained neural network and generate, by the remotecomputing system analyzing the one or more risk indications, aquotation, such as quote for auto insurance. The instructions may causethe computing system to (i) display the quotation to the user, and (ii)provide the quotation as input to a second application. The system mayinclude additional, less, or alternate functionality, including thatdiscussed elsewhere herein.

Advantages will become more apparent to those skilled in the art fromthe following description of the preferred embodiments which have beenshown and described by way of illustration. As will be realized, thepresent embodiments may be capable of other and different embodiments,and their details are capable of modification in various respects.Accordingly, the drawings and description are to be regarded asillustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The figures described below depict various aspects of the system andmethods disclosed therein. It should be understood that each figuredepicts one embodiment of a particular aspect of the disclosed systemand methods, and that each of the figures is intended to accord with apossible embodiment thereof. Further, wherever possible, the followingdescription refers to the reference numerals included in the followingfigures, in which features depicted in multiple figures are designatedwith consistent reference numerals.

There are shown in the drawings arrangements which are presentlydiscussed, it being understood, however, that the present embodimentsare not limited to the precise arrangements and instrumentalities shown,wherein:

FIG. 1 depicts an exemplary computing environment in which techniquesfor training a neural network to identify a risk level of a vehicle maybe implemented, according to one embodiment;

FIG. 2 depicts an exemplary computing environment in which techniquesfor collecting and processing user input, and training a neural networkto identify a risk level of a vehicle may be implemented, according toone embodiment;

FIG. 3 depicts an exemplary artificial neural network which may betrained by the neural network unit of FIG. 1 or the neural networktraining application of FIG. 2, according to one embodiment andscenario;

FIG. 4 depicts an exemplary neuron, which may be included in theartificial neural network of FIG. 3, according to one embodiment andscenario;

FIG. 5 depicts text-based content of an exemplary electronic claimrecord that may be processed by an artificial neural network, in oneembodiment;

FIG. 6 depicts a flow diagram of an exemplary computer-implementedmethod of determining a risk level posed by an operator of a vehicle,according to one embodiment;

FIG. 7 depicts a flow diagram of an exemplary computer-implementedmethod of identifying risk indicators from vehicle operator information,according to one embodiment;

FIG. 8 is a flow diagram depicting an exemplary computer-implementedmethod of detecting and/or estimating damage to personal property,according to one embodiment;

FIG. 9A is an example flow diagram depicting an exemplarycomputer-implemented method of determining damage to personal property,according to one embodiment;

FIG. 9B is an example data flow diagram depicting an exemplarycomputer-implemented method of determining damage to an insured vehicleusing a trained machine learning algorithm to facilitate handling aninsurance claim associated with the damaged insured vehicle, accordingto one embodiment;

FIG. 10A is an example flow diagram depicting an exemplarycomputer-implemented method for determining damage to personal property,according to one embodiment; and

FIG. 10B is an example data flow diagram depicting an exemplarycomputer-implemented method of determining damage to an undamagedinsurable vehicle using a trained machine learning algorithm tofacilitate generating an insurance quote for the undamaged insurablevehicle, according to one embodiment.

The Figures depict preferred embodiments for purposes of illustrationonly. One skilled in the art will readily recognize from the followingdiscussion that alternative embodiments of the systems and methodsillustrated herein may be employed without departing from the principlesof the invention described herein.

DETAILED DESCRIPTION Artificial Intelligence Systems for Insurance

The present embodiments are directed to, inter cilia, machine learningand/or training a model using historical automobile and/or homeinsurance claim data to discover risk levels and price automobileinsurance accordingly. Systems and methods may include natural languageprocessing of free-form notes/text, or free-form speech/audio, recordedby call center and/or claim adjustor, photos, and/or other evidence. Thefree-form text and/or free-form speech may also be received from acustomer who is inputting the text or speech into a mobile device app orinto a smart home controller or smart vehicle controller, and/or into achat bot or robo-advisor.

Other inputs to a machine learning/training model may be harvested fromhistorical claims may, and may include make, model, year, miles,technological features, and/or other characteristics of a vehicle,vehicle operation monitoring systems, whether a claim is paid or notpaid, liability (e.g., types of injuries, where treated, how treated,etc.), disbursements related to claim such as hotel costs and otherpayouts, etc. Additional inputs to the machine learning/training modelmay include vehicle telematics data for automobiles, and for realproperty, home telematics data received from a smart vehicle controller,such as how long and when are the doors unlocked, how often is thesecurity system armed, how long is the vehicle in operation during timeperiods, etc.

The present embodiments may facilitate discovering new causes of lossthat may be utilized to set pricing of insurance. The presentembodiments may dynamically characterize insurance claims, and/ordynamically determine causes of loss associated with insurance claims,which may vary geographically. The present embodiments may alsodynamically update pricing models to facilitate better matchinginsurance premium price to actual risk.

Artificial Intelligence System for Vehicle Insurance

Noted above, the present embodiments may also be directed to machinelearning and/or training a model using historical auto claim data todiscover risk levels, and then price vehicle insurance accordingly. Thepresent embodiments may include natural language processing of free-formnotes recorded by call center and/or claim adjustor (e.g., “hit adeer”), photos, and/or other evidence to use as input to machinelearning/training model. Other inputs to a machine learning/trainingmodel may be harvested from historical claims, and may include make,model, year, claim paid or not paid, liability (e.g., types of injuries,where treated, how treated, etc.), disbursements related to the claimsuch as rental car and other payouts, etc.

Exemplary Environment for Identifying Risk Factors and Calculating Riskin Data

The embodiments described herein may relate to, inter alia, determiningan accurate, granular vehicle insurance risk level corresponding to aplurality of inputs. More particularly, in some embodiments, one or moreneural network models may be trained using historical claims data astraining input. An application may be provided to a client computingdevice (e.g., a smartphone, tablet, laptop, desktop computing device,wearable, or other computing device) of a user. A user of theapplication, who may be an employee of a company employing the methodsdescribed herein or a customer of that company, may enter input into theapplication via a user interface or other means. The input may betransmitted from the client computing device to a remote computingdevice (e.g., one or more servers) via a computer network, and thenprocessed further, including by applying input entered into the clientto the one or more trained neural network models to produce labels andweights indicating net or individual risk factors. The risk factors maybe identified in electronic claim records, and/or may be predictive ofcertain real-world risks. Although historical claims may be used intraining one or more neural network models, electronic claimsinformation may be streaming in realtime or with near-realtime latencies(e.g., on the order of 10 ms or less) along with all input informationto tune the artificial intelligence system, in a dynamic process.

For example, the remote computing device may receive the input anddetermine, using a trained neural network, one or more risk indicatorsapplicable to the input, and/or a risk level. Herein risk indicators maybe expressed numerically, as strings (e.g., as labels), or in any othersuitable format, Risk levels may be expressed as Boolean values (e.g.,risk,/no risk), scaled quantities (e.g., from 0.0-1.0), or in any othersuitable format. The determined risk indicators and/or risk level may bedisplayed to the user, and/or may be provided as input to anotherapplication (e.g., to an application which uses the risk indicators andcalculated risk in a quotation calculation or for other purposes).

A quotation may include a price, parameters describing the vehicle,and/or one or more identified risk indicators, among other information.By transmitting input to the remote computing device for processing andanalysis, an accurate risk level based upon a wealth of historicalknowledge may be determined, and provided to the user in what may appearto the user to be a very rapid, even instantaneous, manner.

Turning to FIG. 1, an exemplary computing environment 100,representative of artificial intelligence platform for vehicleinsurance, is depicted. Environment 100 may include input data 102 andhistorical data 108, both of which may comprise a list of parameters, aplurality (e.g., thousands or millions) of electronic documents, orother information. As used herein, the term “data” generally refers toinformation related to a vehicle operator, which exists in theenvironment 100. For example, data may include an electronic documentrepresenting a vehicle (e.g., automobile, truck, boat, motorcycle, etc.)insurance claim, demographic information about the vehicle operatorand/or information related to the type of vehicle or vehicles beingoperated by the vehicle operator, and/or other information.

Data may be historical or current. Although data may be related to anongoing claim filed by a vehicle operator, in some embodiments, data mayconsist of raw data parameters entered by a human user of theenvironment 100 or which is retrieved/received from another computingsystem.

Data may or may not relate to the claims filing process, and while someof the examples described herein refer to auto insurance claims, itshould be appreciated that the techniques described herein may beapplicable to other types of electronic documents, in other domains. Forexample, the techniques herein may be applicable to identifying riskfactors in other insurance domains, such as agricultural insurance,homeowners insurance, health or life insurance, renters insurance, etc.In that case, the scope and content of the data may differ, in additionto the domain-specific training and operational requirements applicableto the neural network(s).

As another example, data may be collected from an existing customerfiling a claim, a potential or prospective customer applying for aninsurance policy, or may be supplied by a third party such as a companyother than the proprietor of the environment 100. In some cases, datamay reside in paper files that are scanned or entered into a digitalformat by a human or by an automated process e.g., via a scanner).Generally, data may comprise any digital information, from any source,created at any time.

Input data 102 may be loaded into an artificial intelligence system 104to organize, analyze, and process input data 102 in a manner thatfacilitates efficient determination of risk levels by risk levelanalysis platform 106. The loading of input data 102 may be performed byexecuting a computer program on a computing device that has access tothe environment 100, and the loading process may include the computerprogram coordinating data transfer between input data 102 and AIplatform 104 (e.g., by the computer program providing an instruction toAI platform 104 as to an address or location at which input data 102 isstored).

AI platform may reference this address to retrieve records from inputdata 102 to perform risk level determination techniques. AI platform 104may be thought of as a collection of algorithms configured to receiveand process parameters, and to produce labels and, in some embodiments,risk and/or pricing information.

As discussed below with respect to FIGS. 3, 4, and 5; AI platform 104may be used to train multiple neural network models relating todifferent granular segments of vehicle operators. For example, AIplatform 104 may be used to train a neural network model for use byoperators of autonomous vehicles who are over the age of 30. In anotherembodiment, AI platform 104 may be used to train a neural network modelfor use in predicting risk of motorcycle operators in a particular stateor locality. The precise manner in which neural networks are created andtrained is described below.

In the embodiment of FIG. 1, AI platform 104 may include claim analysisunit 120. Claim analysis unit 120 may include speech-to-text unit 122and image analysis unit 124 which may comprise, respectively, algorithmsfor converting human speech into text and analyzing images (e.g.,extracting information from hotel and rental receipts). In this way,data may comprise audio recordings (e.g., recordings made when acustomer telephones a customer service center) that may be converted totext and further used by AI platform 104. In some embodiments, customerbehavior represented in data—including the accuracy and truthfulness ofa customer—may be encoded by claim analysis unit 120 and used by AIplatform 104 to train and operate neural network models.

Claim analysis unit 120 may also include text analysis unit 126, whichmay include pattern matching unit 128 and natural language processing(NLP) unit 130. In some embodiments, text analysis unit 126 maydetermine facts regarding claim inputs (e.g., the amount of money paidunder a claim). Amounts may be determined in a currency- andinflation-neutral manner, so that claim loss amounts may be directlycompared. In some embodiments, text analysis unit 126 may analyze textproduced by speech-to-text unit 122 or image analysis unit 124.

In some embodiments, pattern matching unit 128 may search textual claimdata loaded into AI platform 104 for specific strings or keywords intext (e.g., “hit a deer”) which may be indicative of particular types ofrisk. NLP unit 130 may be used to identify, for example, entities orobjects indicative of risk (e.g., that an injury occurred to a person,and that the person's leg was injured). NLP unit 130 may identify humanspeech patterns in data, including semantic information relating toentities, such as people, vehicles, homes, and other objects.

Relevant verbs and objects, as opposed to verbs and objects of lesserrelevance, may be determined by the use of a machine learning algorithmanalyzing historical claims. For example, both a driver and a deer maybe relevant objects. Verbs indicating collision or injury may berelevant verbs. in some embodiments, text analysis unit 126 may comprisetext processing algorithms (e.g., lexers and parsers, regularexpressions, etc.) and may emit structured text in a format which may beconsumed by other components.

In the embodiment of FIG. 1, AI platform 104 may include a risk levelunit 140 to determine risk based upon analysis of data. Risk may becalculated with respect to individual attributes or elements of data,such as by assigning a risk score between 0 and 1 to a given attribute(e.g., deer). In other embodiments, risk level unit 140 may determine anindication of risk by generating labels which pertain to data in wholeor in part. This labeling may be accomplished in various different ways,depending on the embodiment.

For example, risk level unit 140 may label input data 102, or portionsthereof, according to positive or negative pattern matching according topattern matching unit 128. For example, if input data 102 matches thepattern “hit [a] deer,” wherein the article “a” is optional, then inputdata 102 may receive labels such as (ACCIDENT, DEER) or (COLLISION,ANIMAL). Alternately, in some embodiments, risk level unit 140 may labelinput data 102, which may be raw data or a claim filed by a customer,according to results obtained from natural language processing unit 130(e.g., LIMB-INJURY). Risk level unit 140 may label input data 102according to Boolean values (e.g., PAID/NOT-PAID) or pre-determinedranges (e.g., claims having a payout of 50-$50,000; 550,000-$500,000;$500,000-$1,000,000; or >=$1,000,000).

Labels may be saved to and/or retrieved from an electronic database,such as risk indication data 142, and claim labels may be generated fromalready-existing labels, and/or dynamically created labels (i.e., labelscreated at runtime) by risk level unit 140. A set of labels may beassociated with a set of input data 102, and the creation of new labelsmay be partially or entirely based upon existing labels and/or inputdata 102.

Dynamic creation of labels may, in some embodiments, be based upon userattributes and/or metadata. For example, a resident of the EasternUnited States may be assigned a label related to weather or anotherattribute unique to the region; for example, a hurricane- orflood-related label.

As noted, in some embodiments, risk level unit 140 may analyze inputdata 102 (e.g., label claims) through the use of a neural network unit150. Neural network unit 150 may use an artificial neural network, orsimply “neural network.” The neural network may be any suitable type ofneural network, including, without limitation, a recurrent neuralnetwork or feed-forward neural network. The neural network may includeany number (e.g., thousands) of nodes or “neurons” arranged in multiplelayers, with each neuron processing one or more inputs to generate adecision or other output.

In some embodiments, neural network models may be chained together, sothat output from one model is fed into another model as input. Forexample, risk level unit 140 may, in one embodiment, apply input data102 to a first neural network model that is trained to generate labels.The output (e.g., labels) of this first neural network model may he fedas input to a second neural network model which has been trained topredict claim settlement amounts based upon the presence of labels. Thesecond neural network may be trained using an inflation-adjusted set ofclaim payout amounts, and respective set of risk labels, to veryaccurately predict the amount of money likely to be paid on a new claim,given only a new set of risk labels from the first model.

Neural network unit 150 may include training unit 152, and riskindication unit 154. To train the neural network to identify risk,neural network unit 150 may access electronic claims within historicaldata 108. Historical data 108 may comprise a corpus of documentscomprising many (e.g., millions) of insurance claims which may containdata linking a particular customer or claimant to one or more vehicles,and which may also contain, or be linked to, information pertaining tothe customer. In particular, historical data 108 may be analyzed by AIplatform 104 to generate claim records 110-1 through 110-n, where n isany positive integer. Each claim 110-1 through 110-n may be processed bytraining unit 152 to train one or more neural networks to identify claimrisk factors, including by pre-processing of historical data 108 usinginput analysis unit 120 as described above.

Neural network 150 may, from a trained model, identify labels thatcorrespond to specific data, metadata, and/or attributes within inputdata 102, depending on the embodiment. For example, neural network 150may be provided with instructions from input analysis unit 120indicating that one or more particular type of insurance is associatedwith one or more portions of input data 102.

Neural network 150 may identify one or more insurance types associatedwith the one or more portions of input data 102 (e.g., bodily injury,property damage, collision coverage, comprehensive coverage, liabilityinsurance, med pay, or personal injury protection (PIP) insurance) andby input analysis unit 120. in one embodiment, the one or more insurancetypes may be identified by training the neural network 150 based upontypes of peril. For example, the neural network model may be trained todetermine that fire, theft, or vandalism may indicate comprehensiveinsurance coverage.

In addition, input data 102 may indicate a particular customer and/orvehicle. In that case, risk level unit 140 may look up additionalcustomer and/or vehicle information from customer data 160 and vehicledata 162, respectively. For example, the age of the vehicle operatorand/or vehicle type may be obtained. The additional customer and/orvehicle information may be provided to neural network unit 150 and maybe used to analyze and label input data 102 and, ultimately, may be usedto determine risk. For example, neural network unit 150 may be used topredict risk based upon inputs obtained from a person applying for anauto insurance policy, or based upon a claim submitted by a person whois a holder of an existing insurance policy. That is, in someembodiments where neural network unit 150 is trained on claim data,neural network unit 150 may predict risk based upon raw informationunrelated to the claims filing process, or based upon other dataobtained during the filing of a claim (e.g., a claim record retrievedfrom historical data 108).

In one embodiment, the training process may be performed in parallel,and training unit 152 may analyze all or a subset of claims 110-1through 110-n. Specifically, training unit 152 may train a neuralnetwork to identify claim risk factors in claim records 110-1 through110-n. As noted, AI platform 104 may analyze input data 102 to arrangethe historical claims into claim records 110-1 through 110-n, where n isany positive integer.

Claim records 110-1 through 110-n may be organized in a flat liststructure, in a hierarchical tree structure, or by means of any othersuitable data structure. For example, the claim records may be arrangedin a tree wherein each branch of the tree is representative of one ormore customer. There, each of claim records 110-1 through 110-n mayrepresent a single non-branching claim, or may represent multiple claimrecords arranged in a group or tree.

Further, claim records 110-1 through 110-n may comprise links tocustomers and vehicles whose corresponding data is located elsewhere. Inthis way, one or more claims may be associated with one or morecustomers and one or more vehicles via one-to-many and/or many-to-onerelationships. Risk factors may be data indicative of a particular riskor risks associated with a given claim, customer, and/or vehicle. Thestatus of claim records may be completely settled or in various stagesof settlement.

As used herein, the term “claim” or “vehicle claim” generally refers toan electronic document, record, or file, that represents an insuranceclaim (e.g., an automobile insurance claim) submitted by a policy holderof an insurance company. Herein, “claim data” or “historical data”generally refers to data directly entered by the customer or insurancecompany including, without limitation, free-form text notes,photographs, audio recordings, written records, receipts (e.g., hoteland rental car), and other information including data from legacy,including pre-Internet (e.g., paper file), systems. Notes from claimadjusters and attorneys may also be included. Claim data may includedata entered by third parties, such as information from a repair shop,hospital, doctor, police report, etc.

In one embodiment, claim data may include claim metadata or externaldata, which generally refers to data pertaining to the claim that may bederived from claim data or which otherwise describes, or is related to,the claim but may not be part of the electronic claim record. Claimmetadata may have been generated directly by a developer of theenvironment 100, for example, or may have been automatically generatedas a direct product or byproduct of a process carried out in environment100. For example, claim metadata may include a field indicating whethera claim was settled or not settled, and amount of any payouts, and theidentity of corresponding payees.

Another example of claim metadata is the geographic location in which aclaim is submitted, which may be obtained via a global positioningsystem (GPS) sensor in a device used by the person or entity submittingthe claim. Yet another example of claim metadata includes a category ofthe claim type (e.g., collision, liability, uninsured or underinsuredmotorist, etc.). For example, a single claim in historical data 108 maybe associated with a married couple, and may include the name, address,and other demographic information relating to the couple. Additionally,the claim may be associated with multiple vehicles owned or leased bythe couple, and may contain information pertaining to those vehiclesincluding without limitation, the vehicles' make, model, year,condition, mileage, etc.

The claim may include a plurality of claim data and claim metadata,including metadata indicating a relationship or linkage to other claimsin historical claim data 108. In this way, neural network unit 150 mayproduce a neural network that has been trained to associate the presenceof certain input parameters with higher or lower risk levels. A specificexample of a claim is discussed with respect to FIG. 5, below.

Once the neural network has been trained, risk indication unit 154 mayapply the trained neural network to input data 102 as processed by inputanalysis unit 120. In one embodiment, input analysis unit 120 may merely“pass through” input data 102 without modification. The output of theneural network, indicating risk indications, such as labels pertainingto the entirety of, or portions of input data 102, may then be providedto risk level unit 140. Risk level unit 140 may insert the output of theneural network (e.g., labels) into an electronic database, such as riskindication data 142. Alternatively, or additionally, risk indicationunit 154 may use label information output by the neural network todetermine attributes of input data 102, and may provide those attributesto risk level unit 140.

In some embodiments, each label or attribute may be associated with aconfidence score and/or weight. Confidence scores may be assigned basedupon the source of the information (e.g., if the information is fromvehicle data 274, then a score of 1.0 may be assigned; whereas, if theinformation is inferred and/or provided by a user, a lower confidencescore may be assigned). Risk level unit 140 may then forward the labelsand/or scores to risk level analysis platform 106. In some embodiments,determining a single label may require neural network unit 150 toanalyze several attributes within input data 102. For example, a newcustomer applying for an auto insurance policy may be required toprovide their name, make and model of their car, and a scanned copy oftheir driver's abstract to determine a risk that is reflective of allthree pieces of information. Some models may include validation thatwill produce an error state if a required piece of information is notprovided.

AI platform 104 may further include customer data 160 and vehicle data162, which risk level unit 140 may leverage to provide useful inputparameters to neural network unit 150. Customer data 160 may be anintegral part of AI platform 104, or may be located separately from AIplatform 104. In some embodiments, customer data 160 or vehicle data 162may be provided to AI platform 104 via separate means (e.g., via an APIcall), and may be accessed by other units or components of environment100. Either may be provided by a third-party service.

Vehicle data 162 may be a database comprising information describingvehicle makes and models, including information about model years andmodel types (e.g., model edition information, engine type, any upgradepackages, etc.). Vehicle data 162 may indicate whether certain make andmodel year vehicles are equipped with safety features (e.g., lanedeparture warnings). The vehicle data 162 may also relate to autonomousor semi-autonomous vehicle features or technologies of the vehicle,and/or sensors, software, and electronic components that direct theautonomous or semi-autonomous vehicle features or technologies.

Both of customer data 160 and vehicle data 162 may be used to train aneural network model. For example, to continue the above new customerapplication example, risk level unit 140 may look up the applicant's ageand other demographic information in customer data 160, and may obtainfrom vehicle data 162 the knowledge that the car is a convertible.Further, the driver abstract may be analyzed by image processing unit124 and pattern matching unit 128, which—together—may determine that theapplicant's driver's license was suspended within the prior year.

All of the information pertaining to the applicant may then be providedto neural network unit 150, which may—based upon its prior training onclaims from historical data 108—determine that a plurality of labelsapply to the applicant. For example, the labels may include SUSPENDED,CONVERTIBLE, YOUTH. As noted, the labels may have a respectiveconfidence factor, and may be sorted in terms of criticality, and/orgiven pre-assigned weights. The labels and/or weights may be stored inrisk indication data 142, in an embodiment. It should be appreciatedthat the use of additional vehicle labels (e.g., DIESEL, V8,MANUAL-TRANSMISSION, REVOKED) is envisioned in label generation.

In some embodiments, pattern matching unit 128 and natural languageprocessing unit 130 may act in conjunction to determine labels. Forexample, pattern matching unit 128 may include instructions to identifywords indicating contact (e.g., “hit”, “crash”, or “collide”). Matcheddata may be provided to natural language processing unit 130, which mayfurther process the matched data to determine parts of speech such asverbs and objects, as well as relationships between the objects.

The output of natural language processing unit 130 may be provided toneural network unit 150 and used by training unit 152 to train a neuralnetwork model to label insurance types. For example, if natural languageprocessing unit 152 indicates a collision with an inanimate object, suchas a fence, pole, or otherwise, then the neural network may generate alabel of COLLISION, indicating that the input data 102 may indicate acollision insurance policy. On the other hand, if natural languageprocessing unit 152 indicates a collision with an animal, such as adeer, then the neural network may generate a label of COMPREHENSIVE.

It should be appreciated that in this example, the two labels (COLLISIONand COMPREHENSIVE) are not mutually exclusive. That is, the neuralnetwork model may generate multiple labels corresponding to anindication by pattern matching unit 128 and/or natural languageprocessing unit 130 that both types of insurance coverage are indicated.Further, additional processing, including by the use of an additionalneural network model, maybe used to assign weight to a label. Forexample, a collision involving a deer may receive a higher weight thanone involving a rabbit.

The labels in risk indication data 142 may be provided to risk levelanalysis platform which may perform a calculation using the labelsand/or weights. For example, in one embodiment, risk level analysisplatform 106 may sum the weights and scale the price of a policy offeredto the applicant. In other embodiments, the risk level analysis platform106 may apply a cut-off level, beyond which no policy may be offered. Inyet another embodiment, a maximum and/or minimum weight may be computed,and used to scale a base price.

A maximum or minimum weight may correspond to a local maximum (e.g., thelongest trip taken by a given driver), a global maximum (e.g., thevehicle operator in a vehicle operator cohort with the most claims filedin a five-year period), or a maximum among a set of vehicle operators.It should be appreciated that there are many possibilities for using theinformation generated by the neural network.

In some embodiments, labels may be associated with pre-set weights thatare stored separately from AI platform 104, and which may be updatedindependently. It should also be appreciated that the methods andtechniques described herein may not be applied to seek profit in aninsurance marketplace. Rather, the methods and techniques may be used tomore fairly and equitably allocate risk among customers in a way that isrevenue-neutral, yet which strives for fairness to all marketparticipants, and may only be used on an opt-in basis.

Historically, claim losses may be categorized using loss cause codes.These may be a handful of mutually-exclusive labels or categories intowhich claims are categorized that only permit coarse analysis of risk.

The methods and systems described herein may help risk-averse customersto lower their insurance premiums by more granularly quantifying risk.The methods and systems may also allow new customers to receive moreaccurate pricing when they are shopping for vehicle insurance products.All of the benefits provided by the methods and systems described hereinmay be realized much more quickly than traditional modeling approaches.The methods and systems herein may reduce, in some cases dramatically,insurance company expenses and/or insurance customer premiums, due toincreased efficiencies and improved predictive accuracies.

Exemplary Training Model System

With reference to FIG. 2, a high-level block diagram of vehicleinsurance risk training model system 200 is illustrated that mayimplement communications between a client device 202 and a server device204 via network 206 to provide vehicle insurance loss classificationand/or risk level analysis. FIG. 2 may correspond to one embodiment ofenvironment 100 of FIG. 1, and also includes various user/client-sidecomponents. For simplicity, client device 202 is referred to herein asclient 202, and server device 204 is referred to herein as server 204,but either device may be any suitable computing device (e.g., a laptop,smart phone, tablet, server, wearable device, etc). Server 204 may hostservices relating to neural network training and operation, and may becommunicatively coupled to client 202 via network 206.

Although only one client device is depicted in FIG. 2, it should beunderstood that any number of client devices 202 may be supported.Client device 202 may include a memory 208 and a processor 210 forstoring and executing, respectively, a module 212. While referred to inthe singular, processor 210 may include any suitable number ofprocessors of one or more types (e.g., one or more CPUs, graphicsprocessing units (GPUs), cores, etc.), Similarly, memory 208 may includeone or more persistent memories (e.g., a hard drive and/or solid statememory).

Module 212, stored in memory 208 as a set of computer-readableinstructions, may be related to an input data collection application 216which, when executed by the processor 210, causes input data to bestored in memory 208. The data stored in memory 208 may correspond to,for example, raw data retrieved from input data 102. Input datacollection application 216 may be implemented as web page (e.g., HTML,JavaScript, CSS, etc.) and/or as a mobile application for use on astandard mobile computing platform.

Input data collection application 216 may store information in memory208, including the instructions required for its execution. While theuser is using input data collection application 216, scripts and otherinstructions comprising input data collection application 216 may berepresented in memory 208 as a web or mobile application. The input datacollected by input data collection application 216 may be stored inmemory 208 and/or transmitted to server device 204 by network interface214 via network 206, where the input data may be processed as describedabove to determine a series of risk indications and/or a risk level. Inone embodiment, input data collection application 216 may be data usedto train a model (e.g., scanned claim data).

Client device 202 may also include GPS sensor 218, an image sensor 220,user input device 222 (e.g., a keyboard, mouse, touchpad, and/or otherinput peripheral device), and display interface 224 (e.g., an LEDscreen). User input device 222 may include components that are integralto client device 202, and/or exterior components that arecommunicatively coupled to client device 202, to enable client device202 to accept inputs from the user. Display 224 may be either integralor external to client device 202, and may employ any suitable displaytechnology. In some embodiments, input device 222 and display 224 areintegrated, such as in a touchscreen display. Execution of the module212 may further cause the processor 210 to associate device datacollected from client 202 such as a time, date, and/or sensor data(e.g., a camera for photographic or video data) with vehicle and/orcustomer data, such as data retrieved from customer data 160 and vehicledata 162, respectively.

In some embodiments, client 202 may receive data from risk indicationdata 142 and risk level analysis platform 106. Such data, indicatingrisk labels and/or a risk level computation, may be presented to a userof client 202 by a display interface 224.

Execution of the module 212 may further cause the processor 210 of theclient 202 to communicate with the processor 250 of the server 204 vianetwork interface 214 and network 206. As an example, an applicationrelated to module 212, such as input data collection application 216,may, when executed by processor 210, cause a user interface to bedisplayed to a user of client device 202 via display interface 224. Theapplication may include graphical user input (GUI) components foracquiring data (e.g., photographs) from image sensor 220, GPS coordinatedata from GPS sensor 218, and textual user input from user inputdevice(s) 222,

The processor 210 may transmit the aforementioned acquired data toserver 204, and processor 250 may pass the acquired data to a neuralnetwork, which may accept the acquired data and perform a computation(e.g., training of the model, or application of the acquired data to atrained neural network model to obtain a result). With specificreference to FIG. 1, the data acquired by client 202 may be transmittedvia network 206 to a server implementing AI platform 104, and may beprocessed by input analysis unit 120 before being applied to a trainedneural network by risk level unit 140.

As described with respect to FIG. 1, the processing of input from client202 may include associating customer data 160 and vehicle data 162 withthe acquired data. The output of the neural network may be transmitted,by a risk level unit corresponding to risk level unit 140 in server 204,back to client 202 for display (e.g., in display 224) and/or for furtherprocessing.

Network interface 214 may be configured to facilitate communicationsbetween client 202 and server 204 via any hardwired or wirelesscommunication network, including network 206 which may be a singlecommunication network, or may include multiple communication networks ofone or more types (e.g., one or more wired and/or wireless local areanetworks (LANs), and/or one or more wired and/or wireless wide areanetworks (WANs) such as the Internet). Client 202 may cause insurancerisk related data to be stored in server 204 memory 252 and/or a remoteinsurance related database such as customer data 160.

Server 204 may include a processor 250 and a memory 252 for executingand storing, respectively, a module 254. Module 254, stored in memory252 as a set of computer-readable instructions, may facilitateapplications related to processing and/or collecting insurance riskrelated data, including claim data and claim metadata, and insurancepolicy application data. For example, module 254 may include inputanalysis application 260, risk level application 262, and neural networktraining application 264, in one embodiment.

Input analysis application 260 may correspond to input analysis unit 120of environment 100 of FIG. 1. Risk level application 262 may correspondto risk level unit 140 of environment of FIG. 1, and neural networktraining application 264 may correspond to neural network unit 150 ofenvironment 100 of FIG. 1. Module 254 and the applications containedtherein may include instructions which, when executed by processor 250,cause server 204 to receive and/or retrieve input data from (e.g., rawdata and/or an electronic claim) from client device 202. In oneembodiment, input analysis application 260 may process the data fromclient 202, such as by matching patterns, converting raw text tostructured text via natural language processing, by extracting contentfrom images, by converting speech to text, and so on.

Throughout the aforementioned processing, processor 250 may read datafrom, and write data to, a location of memory 252 and/or to one or moredatabases associated with server 204. For example, instructions includedin module 254 may cause processor 250 to read data from an historicaldata 270, which may be communicatively coupled to server device 204,either directly or via communication network 206. Historical data 270may correspond to historical data 108, and processor 250 may containinstructions specifying analysis of a series of electronic claimdocuments from historical data 270, as described above with respect toclaims 110-1 through 110-n of historical data 108 in FIG. 1.

Processor 250 may query customer data 272 and vehicle 274 for datarelated to respective electronic claim documents and raw data, asdescribed with respect to FIG. 1. In one embodiment customer data 272and vehicle data 274 correspond, respectively, customer data 160 and162. In another embodiment, customer data 272 and/or vehicle data 274may not be integral to server 204. Module 254 may also facilitatecommunication between client 202 and server 204 via network interface256 and network 206, in addition to other instructions and functions.

Although only a single server 204 is depicted in FIG. 2, it should beappreciated that it may be advantageous in some embodiments to provisionmultiple servers for the deployment and functioning of AI system 102.For example, the pattern matching unit 128 and natural languageprocessing unit 130 of input analysis unit 120 may require CPU-intensiveprocessing. Therefore, deploying additional hardware may provideadditional execution speed. Each of historical data 270, customer data272, vehicle data 274, and risk indication data 276 may begeographically distributed.

While the databases depicted in FIG. 2 are shown as beingcommunicatively coupled to server 204, it should be understood thathistorical claim data 270, for example, may be located within separateremote servers or any other suitable computing devices communicativelycoupled to server 204. Distributed database techniques (e.g., shardingand/or partitioning) may be used to distribute data. In one embodiment,a free or open source software framework such as Apache Hadoop® may beused to distribute data and run applications (e.g., risk levelapplication 262). It should also be appreciated that different securityneeds, including those mandated by laws and government regulations, mayin some cases affect the embodiment chosen, and configuration ofservices and components.

In a manner similar to that discussed above in connection with FIG. 1,historical claims from historical claim data 270 may be ingested byserver 204 and used by neural network training application 264 to trainan artificial neural network. Then, when module 254 processes input fromclient 202, the data output by the neural network(s) (e.g., dataindicating labels, risks, weights, etc.) may be passed to risk levelapplication 262. for computation of an overall risk level, which asdiscussed, may be expressed in boolean, decimal, or any other suitableformat. The calculated risk level may then be transmitted to clientdevice 202 and/or another device. The calculated risk level may be usedfor further processing by client device 202, server device 204, oranother device.

It should be appreciated that the client/server configuration depictedand described with respect to FIG. 2 is but one possible embodiment. Insome cases, a client device such as client 202 may not be used. In thatcase, input data may be entered—programmatically, or manually—directlyinto device 204. A computer program or human may perform such dataentry. In that case, device may contain additional or fewer components,including input device(s) and/or display device(s).

The most useful embodiment may vary according to the purpose for whichthe AI platform is being utilized—for example, a different hardwareconfiguration may be preferable if the AI platform is being used toprovide a risk analysis to an end user or customer, whereas anotherembodiment may be preferable if the AI platform is being used to providerisk as part of a backend service. Furthermore, it may be possible topackage the trained neural network for distribution to a client 202(i.e., the trained neural network may be operated on the client 202without the use of a server 204).

In operation, the user of client device 202, by operating input device222 and viewing display 224, may open input data collection application216, which depending on the embodiment, may allow the user to enterpersonal information. The user may be an employee of a companycontrolling AI platform 104 or a customer or end user of the company.For example, input data collection application 216 may walk the userthrough the steps of submitting a claim.

Before the user can fully access input data collection application 216,the user may be required to authenticate (e.g., enter a valid usernameand password). The user may then utilize input data collectionapplication 216. Module 212 may contain instructions that identify theuser and cause input data collection application 216 to present aparticular set of questions or prompts for input to the user, based uponany information input data collection application 216 collects,including without limitation information about the user or any vehicle.

Further, module 212 may identify a subset of historical data 270 to beused in training a neural network, and/or may indicate to server device204 that the use of a particular neural network model or models isappropriate. For example, if the user is applying for liability vehicleinsurance on a particular make and model year car, then module 212 maytransmit the user's name and personal information, the location of theuser as provided by GPS 218, a photograph of the vehicle to be insuredcaptured by image sensor 220; and the make, model, and year of thevehicle to server device 204.

In some embodiments, location data from client device 202 may be used bya neural network to label risk, and labels may be linked, in that afirst label implies a second label. As noted above, location may beprovided to one or more neural networks in the AI platform to generatelabels and determine risk. For example, the zip code of a vehicleoperator, whether provided via GPS or entered manually by a user, maycause the neural network to generate a label applicable to the vehicleoperator such as RURAL, SUBURBAN, or URBAN.

Such qualifications may be used in the calculation of risk, and may beweighted accordingly. For example, the neural network may assign ahigher risk weight to the RURAL label, due to the increased likelihoodof collision with animals. Due to the increased risk of collision withanimals, the generation of a RURAL label may be accompanied byadditional labels such as COLLISION. Alternatively, or in addition, thecollision label weight may be increased along with the addition of theRURAL label.

Another label, such as LONG-TRIP, to reflect that the vehicle operatordrives longer trips than other drivers, on average, may be associatedwith vehicle operators who the neural network labels as RURAL. In someembodiments, label generation may be based upon seasonal information, inwhole or in part. For example, the neural network may generate labels,and/or adjust label weights based upon location provided in input data.The trained neural network model may learn to associate drivers whodrive in the city in summer with higher risk.

All other inputs being equal, vehicle operator risk may differ basedupon the time of year when the vehicle operator is applying forinsurance. It should be appreciated that the quick and automaticgeneration of such associations is a benefit of the methods and systemsdisclosed herein, and that some of the associations may appearcounter-intuitive when analyzing large data sets.

By the time the user of client 202 submits an application for vehicleinsurance or files a claim, server 204 may have already processed theelectronic claim records in historical data 270 and trained a neuralnetwork model to analyze the information provided by the user to outputrisk indications, labels, and/or weights.

For example, the operator of a 2012 Jeep Cherokee may access client 202to submit a claim under the driver's collision insurance policy relatedto damage to the vehicle sustained while the driver was on vacation in astate other than the driver's home state. Client 202 may collectinformation from the vehicle operator related to the circumstances ofthe collision, in addition to demographic information of the vehicleoperator, including location and photographs from GPS 218 and imagesensor 220, respectively. In some embodiments, the vehicle operator maybe prompted to make a telephone call to discuss the filing of the claim,which may be recorded and later provided to server 204.

All of the information collected may be associated with a claimidentification number so that it may be referenced as a whole. Server204 may process the information as it arrives, and thus may processinformation collected by input data collection application 216 at adifferent time than server 204 processes the audio recording in theabove example. Once information sufficient to process the claim has beencollected, server 204 may pass all of the processed information (e.g.,from input analysis application) to risk level application 262, whichmay apply the information to the trained neural network model.

While the claim or application processing is pending, client device 202may display an indication that the processing of the claim is ongoingand/or incomplete. When the claim is ultimately processed by server 204,an indication of completeness may be transmitted to client 202 anddisplayed to user, for example via display 224. Missing information maycause the model to abort with an error.

In some embodiments, the labels and/or characterization of input data(claims and otherwise) performed by the systems and methods describedherein may be capable of dynamic, incremental, and or online training.Specifically, a model that has been trained on a set of electronic claimrecords from historical data 270 may be updated dynamically, such thatthe model may be updated on a much shorter time scale. For example, themodel may be adjusted weekly or monthly to take into accountnewly-settled claims.

In one embodiment, the settlement of a claim may trigger an immediateupdate of one or more neural network models included in the AI platform.For example, the settlement of a claim involving personal injury thatoccurs on a boat may trigger updates to a set of personal injury neuralnetwork models pertaining to boat insurance. In addition, oralternatively, as new claims are filed and processed, new labels may bedynamically generated, based upon risks identified and generated duringthe training process. In some embodiments, a human reviewer or team ofreviewers may be responsible for approving the generated labels and anyassociated weightings before they are used.

In some embodiments, AI platform 104 may be trained and/or updated toprovide one or more dynamic insurance rating models which may beprovided to, for example, a governmental agency. As discussed above,models are historically difficult to update and updates may be performedon a yearly basis. Using the techniques described herein, models may bedynamically updated in real-time, or on a shorter schedule (e.g.,weekly) based upon new claim data.

While FIG. 2 depicts a particular embodiment, the various components ofenvironment 100 may interoperate in a manner that is different from thatdescribed above, and/or the environment 100 may include additionalcomponents not shown in FIG. 2. For example, an additionalserver/platform may act as an interface between client device 202 andserver device 204, and may perform various operations associated withproviding the labeling and/or risk analysis operations of server 204 toclient device 202 and/or other servers.

Exemplary Artificial Neural Network

FIG. 3 depicts an exemplary artificial neural network 300 which may betrained by neural network unit 150 of FIG. 2 or neural network trainingapplication 264 of FIG. 2, according to one embodiment and scenario. Theexample neural network 300 may include layers of neurons, includinginput layer 302, one or more hidden layers 304-1 through 304-n, andoutput layer 306. Each layer comprising neural network 300 may includeany number of neurons i.e., q and r may be any positive integers. Itshould be understood that neural networks may be used to achieve themethods and systems described herein that are of a different structureand configuration than those depicted in FIG. 3.

Input layer 302 may receive different input data. For example, inputlayer 302 may include a first input a₁ which represents an insurancetype (e.g., collision), a second input a₂ representing patternsidentified in input data, a third input a₃ representing a vehicle make,a fourth input a₄ representing a vehicle model, a fifth input a₅representing whether a claim was paid or not paid, a sixth input a₆representing an inflation-adjusted dollar amount disbursed under aclaim, and so on. Input layer 302 may comprise thousands or more inputs.In some embodiments, the number of elements used by neural network 300may change during the training process, and some neurons may be bypassedor ignored if, for example, during execution of the neural network, theyare determined to be of less relevance.

Each neuron in hidden layer(s) 304-1 through 304-n may process one ormore inputs from input layer 302, and/or one or more outputs from aprevious one of the hidden layers, to generate a decision or otheroutput. Output layer 306 may include one or more outputs each indicatinga label, confidence factor, and/or weight describing one or more inputs.A label may indicate the presence (ACCIDENT, DEER) or absence (DROUGHT)of a condition. in some embodiments, however, outputs of neural network300 may be obtained from a hidden layer 304-1 through 304-n in additionto, or in place of, output(s) from output layer(s) 306.

In some embodiments, each layer may have a discrete, recognizable,function with respect to input data. For example, if n=3, a first layermay analyze one dimension of inputs, a second layer a second dimension,and the final layer a third dimension of the inputs, where alldimensions are analyzing a distinct and unrelated aspect of the inputdata. For example, the dimensions may correspond to aspects of a vehicleoperator considered strongly determinative, then those that areconsidered of intermediate importance, and finally those that are ofless relevance.

In other embodiments, the layers may not be clearly delineated in termsof the functionality they respectively perform. For example, two or moreof hidden layers 304-1 through 304-n may share decisions relating tolabeling, with no single layer making an independent decision as tolabeling.

In some embodiments, neural network 300 may be constituted by arecurrent neural network, wherein the calculation performed at eachneuron is dependent upon a previous calculation. It should beappreciated that recurrent neural networks may be more useful inperforming certain tasks, such as automatic labeling of images.Therefore, in one embodiment, a recurrent neural network may be trainedwith respect to a specific piece of functionality with respect toenvironment 100 of FIG. 1. For example, in one embodiment, a recurrentneural network may be trained and utilized as part of image processingunit 124 to automatically label images.

FIG. 4 depicts an example neuron 400 that may correspond to the neuronlabeled as “1,1” in hidden layer 304-1 of FIG. 3, according to oneembodiment. Each of the inputs to neuron 400 (e.g., the inputscomprising input layer 302) may be weighted, such that input a₁ througha_(p) corresponds to weights w₁ through w_(p), as determined during thetraining process of neural network 300.

In some embodiments, some inputs may lack an explicit weight, or may beassociated with a weight below a relevant threshold. The weights may beapplied to a function α, which may be a summation and may produce avalue z₁ which may be input to a function 420, labeled as ƒ_(1,1)(z₁).The function 420 may be any suitable linear or non-linear, or sigmoid,function. As depicted in FIG. 4, the function 420 may produce multipleoutputs, which may be provided to neuron(s) of a subsequent layer, orused directly as an output of neural network 300. For example, theoutputs may correspond to index values in a dictionary of labels, or maybe calculated values used as inputs to subsequent functions.

It should be appreciated that the structure and function of the neuralnetwork 300 and neuron 400 depicted are for illustration purposes only,and that other suitable configurations may exist. For example, theoutput of any given neuron may depend not only on values determined bypast neurons, but also future neurons.

Exemplary Processing of a Claim

The specific manner in which the one or more neural networks employmachine learning to label and/or quantify risk may differ depending onthe content and arrangement of training documents within the historicaldata (e.g., historical data 108 of FIG. 1 and historical data 270 ofFIG. 2) and the input data provided by customers or users of the AIplatform (e.g., input data 102 of FIG. 1 and the data collected by inputdata collection application 216 of FIG. 2), as well as the data that isjoined to the historical data and input data, such as customer data 160of FIG. 1 and customer data 272 of FIG. 2, and customer data 160 of FIG.1 and vehicle data 274 of FIG. 2.

The initial structure of the neural networks (e.g., the number of neuralnetworks, their respective types, number of layers, and neurons perlayer, etc.) may also affect the manner in which the trained neuralnetwork processes the input and claims. Also, as noted above, the outputproduced by neural networks may be counter-intuitive and very complex.For illustrative purposes, intuitive and simplified examples will now bediscussed in connection with FIG. 5.

FIG. 5 depicts text-based content of an example electronic claim record500 which may be processed using an artificial neural network, such asneural network 300 of FIG. 3 or a different neural network generated byneural network unit 150 of FIG. 1 or neural network training application264 of FIG. 2. The term “text-based content” as used herein includesprinting (e.g., characters A-Z and numerals 0-9), in addition tonon-printing characters (e.g., whitespace, line breaks, formatting, andcontrol characters). Text-based content may be in any suitable characterencoding, such as ASCII or UTF-8 and text-based content may includeHTML.

Although text-based-content is depicted in the embodiment of FIG. 5, asdiscussed above, claim input data may include images, includinghand-written notes, and the AI platform may include a neural networktrained to recognize hand-writing and to convert hand-writing to text.Further, “text-based content” may be formatted in any acceptable dataformat, including structured query language (SQL) tables, flat files,hierarchical data formats (e.g., XML, JSON, etc) or as other suitableelectronic objects. In some embodiments, image and audio data may be teddirectly into the neural network(s) without being converted to textfirst.

With respect to FIG. 5, electronic claim record 500 includes threesections 510 a-510 c, which respectively represent policy information,loss information, and external information. Policy information 510 a mayinclude information about the insurance policy under which the claim hasbeen made, including the person to whom the policy is issued, the nameof the insured and any additional insureds, the location of the insured,etc. Policy information 510 a may be read, for example by input analysisunit 120 analyzing historical data such as historical data 108 andindividual claims, such as claims 110-1 through 110-n. Similarly,vehicle information may be included in policy information 510 a, such asa vehicle identification number (VIN).

Additional information about the insured and the vehicle (e.g., make,model, and year of manufacture) may be obtained from data sources andjoined to input data. For example, additional customer data may beobtained from customer data 160 or customer data 272, and additionalvehicle data may be obtained from vehicle data 162 and vehicle data 274.in some embodiments, make and model information may be included inelectronic claim record 500, and the additional lookup may be of vehicleattributes (e.g., the number of passengers the vehicle seats, theavailable options, etc.).

In addition to policy information 510 a, electronic claim record 500 mayinclude loss information 510 b. Loss information generally correspondsto information regarding a loss event in which a vehicle covered by thepolicy listed in policy information 510 a sustained loss, and may be dueto an accident or other peril. Loss information Slob may indicate thedate and time of the loss, the type of loss (e.g., whether collision,comprehensive, etc.), whether personal injury occurred, whether theinsured made a statement in connection with the loss, whether the losswas settled, and if so for how much money.

In some embodiments, more the than one loss may be represented in lossinformation 510 b. For example, a single accident may give rise tomultiple losses under a given policy, for example to two vehiclesinvolved in a crash operated by vehicle operators not covered under thepolicy. In addition to loss information, electronic claim record 500 mayinclude external information 510 c, including but not limited tocorrespondence with the vehicle operator, statements made by the vehicleoperator, etc. External information 510 c may be textual, audio, orvideo information. The information may include file name references, ormay be file handles or addresses that represent links to other tiles ordata sources, such as linked data 520 a-g. It should be appreciated thatalthough only links 520 a-g are shown, more or fewer links may beincluded, in some embodiments.

Electronic claim record 500 may include links to other records,including other electronic claim records. For example, electronic claimrecord 500 may link to notice of loss 520 a, one or more photographs 520b, one or more audio recordings 520 c, one or more investigator'sreports 520 d, one or more forensic reports 520 e, one or more diagrams520 f, and one or more payments 520 g. Data in links 520 a-520 g may beingested by an AI platform such as AI platform 120. For example, asdescribed above, each claim may be ingested and analyzed by inputanalysis unit 120.

AI platform 104 may include instructions which cause input analysis unit120 to retrieve, for each link 520 a-520 g, all available data or asubset thereof. Each link may be processed according to the type of datacontained therein; for example, with respect to FIG. 1, input analysisunit 120 may process, first, all images from one or more photograph 520b using image processing unit 124 Input analysis unit 120 may processaudio recording 520 c using speech-to-text unit 122.

In some embodiments, a relevance order may be established, andprocessing may be completed according to that order. For example,portions of a claim that are identified as most dispositive of risk maybe identified and processed first. If, in that example, they aredispositive of pricing, then processing of further claim elements may beabated to save processing resources. In one embodiment, once a givennumber of labels is generated (e.g., 50) processing may automaticallyabate.

Once the various input data comprising electronic claim record 500 hasbeen processed, the results of the processing may, in one embodiment, bepassed to a text analysis unit, and then to neural network. If the AIplatform is being trained, then the output of input analysis unit 120may be passed directly to neural network unit 150. The neuronscomprising a first input layer of the neural network being trained byneural network unit 150 may be configured so that each neuron receivesparticular input(s) which may correspond, in one embodiment, to one ormore pieces of information from policy information 510 a, lossinformation 510 b, and external information 510 c.

Similarly, one or more input neurons may be configured to receiveparticular input(s) from links 520 a-520 g. If the AI platform is beingused to accept input to predict a claim value during the claims filingprocess, or to estimate the risk posed by a new customer during theapplication process, then the processing may begin with the use of aninput collection application, as discussed with respect to oneembodiment in FIG. 2.

In some embodiments, analysis of input entered by a user may beperformed on a client device, such as client device 202. In that case,output from input analysis may be transmitted to a server, such asserver 204, and may be passed directly as input to neurons of analready-trained neural network, such as a neural network trained byneural network training application 264.

In one embodiment, the value of a new claim may be predicted directly bya neural network model trained on historical data 108, without the useof any labeling. For example, a neural network may be trained such thatinput parameters correspond to, for example, policy information 510 a,loss information 512 b, external information 512 c, and linkedinformation 520 a-520 g.

The trained model may be configured so that inputting sample parameters,such as those in the example electronic claim record 500, may accuratelypredict, for example, the estimate of damage ($25,000) and settledamount ($24,500). In this case, random weights may be chosen for allinput parameters.

The model may then be provided with training data from claims 110-1through 110-n, which are each pre-processed by the techniques describedherein with respect to FIGS. 1 and 2 to extract individual inputparameters. The electronic claim record 500 may then be tested againstthe model, and the model trained with new training data claims, untilthe predicted dollar values and the correct dollar values converge.

In one embodiment, the AI platform may modify the information availablewithin an electronic claim record. For example, the AI platform maypredict a series of labels as described above that pertain to a givenclaim. The labels may be saved in a risk indication data store, such asrisk indication data 142 with respect to FIG. 1. Next, the labels andcorresponding weights, in one embodiment, may be received by risk levelanalysis platform 106, where they may be used in conjunction with baserate information to predict a claim loss value.

In some embodiments, information pertaining to the claim, such as thecoverage amount and vehicle type from policy information 510 a, may bepassed along with the labels and weights to risk analysis platform 106and may be used in the computation of a claim loss value. After theclaim loss value is computed, it may be associated with the claim, forexample by writing the amount to the loss information section of theelectronic claim record (e.g., to the loss information section 510 b ofFIG. 5).

As noted above, the methods and systems described herein may be capableof analyzing decades of electronic claim records to build neural networkmodels, and the formatting of electronic claim records may changesignificantly from decade to decade, even year to year. Therefore, it isimportant to recognize that the flexibility built into the methods andsystems described herein allows electronic claim records in disparateformats to be consumed and analyzed.

Exemplary Computer-Implemented Methods

Turning to FIG. 6, an exemplary computer-implemented method 600 fordetermining a risk level posed by an operator of a vehicle is depicted.The method 600 may be implemented via one or more processors, sensors,servers, transceivers, and/or other computing or electronic devices. Themethod 600 may include training a neural network to identify riskfactors that are predictive of electronic vehicle claim records (e.g.,by an AI platform such as AI platform 104 training a neural network byan input analysis unit 120 processing data before passing the results ofthe analysis to a training unit 152 that uses the results to train aneural network model) (block 610), The method 600 may include receivinginformation corresponding to the vehicle by an AI platform (e.g., the AIplatform 104 may accept input data such as input data 102 and mayprocess that input by the use of an input analysis unit such as inputanalysis unit 120) (block 620). The method 600 may include analyzing theinformation using the trained neural network (e.g., a risk indicationunit 154 applies the output of the input analysis unit 120 to trainedneural network model) to generate one or more risk indicatorscorresponding to the information (e.g., the neural network produces aplurality of labels and/or corresponding weights) (block 630) which areused to determine a risk level corresponding to the vehicle based uponthe one or more risk indicators (e.g., risk indications are stored inrisk indication data 142, and/or passed to risk level analysis platform106 for computation of a risk level, which may be based upon weightsalso generated by the trained neural network) (block 640). The methodmay include additional, less, or alternate actions, including thosediscussed elsewhere herein.

Turning to FIG. 7, a flow diagram for an exemplary computer-implementedmethod 700 of determining risk indicators from vehicle operatorinformation. The method 700 may be implemented by a processor (e.g.,processor 250) executing, for example, a portion of AI platform 104,including input analysis unit 120, pattern matching unit 128, naturallanguage processing unit 130, and neural network unit 150. Inparticular, the processor 220 may execute an input data collectionapplication 216 and an input device 222 to cause the processor 225 toacquire application input 710 from a user of a client 202.

The processor 220 may further execute the input data collectionapplication 216 to cause the processor 220 to transmit application input710 from the user via network interface 214 and a network 206 to aserver (e.g., server 204). Processor 250 of server 204 may cause module254 of server 204 to process application input 710. input analysisapplication 260 may analyze application input 710 according to themethods describe above. For example, vehicle information maybe queriedfrom a vehicle data such as vehicle data 274. A VIN number inapplication input 710 may be provided as a parameter to vehicle data274.

Vehicle data 274 may return a result indicating that a correspondingvehicle was found in vehicle data 274, and that it is a gray minivanthat is one year old. Similarly, the purpose provided in applicationinput 710 may be provided to a natural language processing unit (e.g.,NLP unit 130), which may return a structured result indicating that thevehicle is being driven by a person who is an employed student athlete.The result of processing the application input 710 may be provided to arisk level unit (e.g., risk level unit 140) which will apply the inputparameters to a trained neural network model.

In one embodiment, the trained neural network model may produce a set oflabels and confidence factors 720. The set of labels and confidencefactors 720 may contain labels that are inherent in the applicationinput 710 (e.g., LOW-MILEAGE) or that are queried based upon informationprovided in the application input 710 (e.g., MINIVAN, based upon VIN).However, the set of labels and confidence factors 720 may includeadditional labels (e.g., COLLISION and DEER) that are not evident fromthe application input 710 or any related/queried information. Afterbeing generated by the neural network, the set of labels and confidencefactors 720 may then be saved to an electronic database such as riskindication data 276, and/or passed to a risk level analysis platform106, whereupon a total risk may be computed and used in a pricing quoteprovided to the user of client 202.

It should be appreciated that many more types of information may beextracted from the application input 710 (e.g., from example links 520a-520 g as shown in FIG. 5). In one embodiment, the pricing quote may bea weighted average of the products of label weights and confidences. Themethod 700 may be implemented, for example, in response to a vehicleoperator accessing client 202 for the purpose of applying for aninsurance policy, or adding (via an application) an additional insuredto an existing policy. The method may include additional, less, oralternate actions, including those discussed elsewhere herein.

With respect to FIG. 8, a flow diagram for an exemplarycomputer-implemented method 800 of detecting and/or estimating damage topersonal property is depicted, according to an embodiment. The method800 may be implemented, for instance, by a processor (e.g., processor250) executing, for example, a portion of AI platform 104, includinginput analysis unit 120, pattern matching unit 128, natural languageprocessing unit 130, and neural network unit 150. In particular, theprocessor 250 may execute an input analysis application 260 to causeprocessor 250 to receive free-form text or voice/speech associated witha submitted insurance claim for a damaged insured vehicle (block 802).The method may include identifying one or more key words within thefree-form text or voice/speech (block 804). The identification of keywords within free-form text may be performed by a module of AI platform104 (e.g., by text analysis unit 126, pattern matching unit 128, and/ornatural language processing unit 130). The identification of key wordswithin voice/speech may be performed by, for example, speech-to-textunit 122. The method may further include determining a cause of lossand/or peril that caused damage to the damaged insured vehicle (block806). A cause of loss and/or peril may be chosen from a set of causes ofloss known to the insurer (e.g., a set stored in risk indication data142) or may be identified or generated by risk indication unit 154.

In some embodiments, the free-form text may be associated with a webpageor user interface of a client device accessed by a customer or employeeof the proprietor of AI system 104 (e.g., an insurance agent) or by auser interface of an intranet page accessed by an employee of a callcenter. For example, the free-form text may be entered by a personutilizing input device 222 and display 224 of client device 202, and theinput may be caused to be collected by processor 210 executinginstructions in input data collection application 216. Voice/speech of auser may be collected by processor 210 causing instructions in inputdata collection 216 to be executed which read audio signals from aninput device such as a microphone. In one embodiment, free-form text orvoice/speech may be input to server device 204 via other means (e.g.,directly loaded onto server device 204). In some embodiments, a neuralnetwork may be trained (e.g., by neural network training unit 264) toidentify, or determine, a key word (or words) associated with a cause ofloss and/or peril using free-form text or voice/speech and a typecorresponding to the insured vehicle as training data. For example,multiple neural networks may be trained that individually correspond tomultiple different respective vehicle types and sets of free-form textor voice/speech.

In one embodiment, the machine learning algorithms may be dynamically orcontinuously trained (i.e., trained online) to dynamically update a setof key words associated with respective cause of loss and/or perilinformation. The cause of loss and/or peril information may be similarlydynamically updated. Such a dynamic set may be stored and updated in anelectronic database, such as risk indication data 276.

In one embodiment, a first cause of loss and/or first a peril may beidentified, and an image may be received. For example, a user maycapture an image, e.g., a digital image, of a vehicle (e.g., a vehiclethat is damaged and/or insured) via image sensor 220, or other type ofcamera. The image may be collected by module 212 and transmitted vianetwork interface 214 and network 206 to network interface 256,whereupon the image may be analyzed by input analysis application 260.The image may be input to neural network unit 150 and passed to atrained neural network model or algorithm, which may analyze the imagedetermine a second cause of loss and/or second peril. Then, the firstcause of loss and/or peril (e.g., that were identified in a free-formsubmission, such as a claim) may be compared to the second cause of lossand/or peril corresponding to the image, to verify the accuracy of thesubmitted claim and/or to identify potential fraud or inflation ofotherwise legitimate claims. In some embodiments the image received viaimage sensor 220 may be analyzed to estimate damages, in terms of costand/or severity. Repair and replacement cost may be determined, in oneembodiment, by training a neural network model to accept an image of adamaged vehicle, and to output an estimate of the severity or cost ofdamages, repair, and/or replacement cost. Such models may be trainedusing the methods described herein including, without limitation, usinga subset of historical data 108 as training data.

In some embodiments, an insurance policy associated with the damagedinsured vehicle may be received or retrieved. The cause of loss and/orperil may be analyzed to determine whether the cause of loss and/orperil are covered under the insurance policy. For example, a user ofclient device 202 may be required to login to an application in module212 using a username or password. The user may be prompted to upload animage of a damaged vehicle during the claims submission process by theapplication in module 212, and the user may do so by capturing an imageof a damaged vehicle the user owns via image sensor 220. The image, andan indication of the user's identity, may be transmitted via network 206to server device 204.

Server device 204 may determine the cause of loss as described above byanalyzing the image, and may retrieve an insurance policy correspondingto the user by querying, for example, customer data 272. Server 204 maycontain instructions that cause the cause of loss or peril associatedwith the uploaded image to be analyzed in light of the insurance policy.The insurance policy may be machine readable, such at the cause of lossand peril information is directly comparable to the insurance policy.

In one embodiment, another means of comparison may be employed (e.g., adeep learning or Bayesian approach). Server 204, or more precisely anapplication executing in server 204, may then determine whether or not,or to what extent, the cause of loss associated with the image capturedby the user is covered under the user's insurance policy. in oneembodiment, an indication of the coverage may be transmitted to the user(e.g., via network 206). The causes of loss, perils, and keywords/concepts that may be identified and/or determined by theabove-described methods include, without limitation: collision,comprehensive, bodily injury, property damage, liability, medical,rental, towing, and ambulance.

FIG. 9A is an example flow diagram depicting an exemplarycomputer-implemented method 900 of determining damage to personalproperty, according to one embodiment. The method 900 may includeinputting historical claim data into a machine learning algorithm, ormodel, to train the algorithm to identify an insured vehicle, a type ofinsured vehicle, vehicle features or characteristics, a peril associatedwith the vehicle, and/or a cost associated with the vehicle (block 902).The method 900 may be implemented by a processor (e.g., processor 250)executing, for example, a portion of AI platform 104, including inputanalysis unit 120, and/or otherwise implemented via, for instance, oneor more processors, sensors, servers, and/or transceivers. Processor 250may execute an input analysis application 260 to cause processor 250 toreceive an image of the damaged insured vehicle (block 904).

The method may further include inputting an image of the damaged insuredvehicle into the trained machine learning algorithm to identify a typeof insured vehicle, vehicle features or characteristics, perilassociated with the vehicle, and/or a cost associated with the vehicle.A type of vehicle may include any attribute of the vehicle, includingwithout limitation, whether the body type (e.g., coupe, sedan), make,model, model year, options (e.g., sport package), whether the vehicle isautonomous or not, etc. In some embodiments, the features andcharacteristics may include an indication of whether the vehicleincludes autonomous or semi-autonomous technologies or systems. In someembodiments, the peril associated with the damaged insured vehicle maycomprise collision, comprehensive, fire, water, smoke, hail, wind, orstorm surge.

In one embodiment, an insurance policy associated with the damagedinsured vehicle may be retrieved by AI platform 104, for example, fromcustomer data 160, and the type of peril compared to the insurancepolicy to determine whether or not the peril is a covered peril underthe insurance policy. As noted above, the applicable policy may beidentified by a user identification passed from a client device, but insome embodiments, the applicable policy may be identified by othermeans. For example, a VIN number or license plate may be digitized byoptical character recognition (e.g., by image processing unit 124) fromthe image provided to the AI platform 104, and the digitization used tosearch customer data 160 for a matching insurance policy.

FIG. 9B is an example data flow diagram depicting an exemplarycomputer-implemented method 910 of determining damage to an insuredvehicle using a trained machine learning algorithm to facilitatehandling an insurance claim associated with the damaged insured.vehicle, according to one embodiment. The method 910 may be implemented,for instance, via one or more processors, sensors, servers,transceivers, and/or other computing or electronic devices.

The method 910 may include receiving a photograph of a damaged insuredvehicle 912. The image may be received by, for example, image processingunit 124 of AI platform 104. The image may originate in a sensor of aclient device, such as image sensor 220 of client device 202, and may becaptured in response to an action taken by a user, such as the userpressing a user interface button (e.g., a button or screen element ofinput device 222). The photograph may be analyzed by image processingunit 124 (e.g., sharpened, contrasted, or converted to a dot matrix)before being passed to neural network unit 150, where it may be input toa trained machine learning algorithm, or neural network model (block914). The trained neural network model in block 914 may correspond tothe machine learning algorithm trained in block 904 of FIG. 9A, Themethod may include identifying information 916 which may include a typeof the damaged insured vehicle, a respective feature or characteristicof the damaged insured vehicle, a peril associated with the damagedinsured vehicle, and/or a repair or replacement cost associated with thedamaged insured vehicle. The information 916 may be used to facilitatehandling an insurance claim associated with the damaged insured vehicle.

FIG. 10A is an example flow diagram depicting an exemplarycomputer-implemented method 1000 for determining damage to personalproperty, according to one embodiment. The method 1000 may beimplemented, for instance, via one or more processors, sensors, servers,transceivers, and/or other computing or electronic devices.

The method 1000 may include inputting historical claim information intoa machine learning algorithm, or model, to train the algorithm todevelop a risk profile for an undamaged insurable vehicle based upon atype, feature, and/or characteristic of the vehicle (block 1002), Thetype, feature, and/or characteristic of the vehicle may include anindication of the geographic area of the vehicle, the vehicle make ormodel, information about the vehicle's transmission, information aboutthe type and condition of the vehicle's tires, information about thevehicle's engine, information pertaining to whether the vehicle includesautonomous or semi-autonomous features, information about the vehicle'sair conditioning or lack thereof, information specifying whether thevehicle has power brakes and windows, and the color of the vehicle. Themethod may further include receiving an image of an undamaged insurablevehicle (block 1004). The method may further include inputting the imageof the undamaged insurable vehicle into a machine learning algorithm toidentify a risk profile for the undamaged insurable vehicle (block1006).

A risk profile may include a predicted loss amount, likelihood of loss,or a risk relative to other vehicles. For example, for a minivan may belower than a risk profile for a sports car. Similarly, the risk of beingrear-ended in a sports car may be lower than the risk of beingrear-ended in a minivan. A risk profile may also include multiple riskswith respect to one or more peril (e.g., respective risks for collision,liability, and comprehensive) in addition to an overall, or aggregate,risk profile.

FIG. 10B is an example data flow diagram depicting an exemplarycomputer-implemented method 1010 of using a trained machine learningalgorithm to facilitate generating an insurance quote for an undamagedinsurable vehicle, according to one embodiment. The method 1010 may beimplemented, for instance, via one or more processors, sensors, servers,transceivers, and/or other computing or electronic devices.

The method may include receiving an image, or photograph, of anundamaged vehicle 1012. The photograph may originate in a client device,such as client 202, and may be captured and transmitted to a server viathe methods described above. The method 1010 may include inputting theimage of an undamaged vehicle into a trained machine learning algorithm1014. The trained neural network may correspond to the neural networktrained block 1002 of FIG. 10A, and the machine learning algorithm maybe trained using historical claim information corresponding tohistorical data 108 of FIG. 1. The neural network may be configured toaccept historical claim data and to predict damage amounts, or otherrisks.

The method may include inputting the image of the undamaged insurablevehicle into the trained machine learning algorithm to identify a riskprofile for the undamaged insurable vehicle, wherein the risk profilemay correspond to the risk profile described above with respect to block1006. It should be appreciated that the use of neural networks may causevariables to emerge from large data sets that are not expected, butwhich are highly correlated to risk. In sonic cases, the risk profileassociated with a given vehicle may contain information that seemsunforeseeable and/or counter-intuitive.

In one embodiment, the risk profile described above may be used togenerate an insurance policy and/or determine a rate quotationcorresponding to the undamaged insurable vehicle wherein the policyand/or rate are based upon the risk profile. In one embodiment, the ratemay include a usage-based insurance (UBI) rate. In some embodiments, thegenerated insurance policy and/or rate quotation may be transmitted tothe vehicle owner for a review and/or approval process. For example, auser of client device 202 may submit an image of their vehicle viaprocessor 210 and module 212, and the above-described analysis involvingthe trained neural network model may then take place on server 204.Then, when a rate quote or policy is generated on the server, the quoteor policy may be transmitted by network interface 256 to network 206 andultimately to network interface 214, back on the client.

The client may include an application in module 212 which causes thepolicy or rate to be displayed to the user of client 202 (e.g., viadisplay 224), and the user may review the policy/quote, and may beprompted to enter (e.g., via input device 222) their approval with theterms of the policy/quote. The user's approval may be transmitted backto the server 204 via network 206, and a contract for insurance formed.In this way, a user may successfully register for an insurance policycoveting an insurable vehicle, by capturing an image of the vehicle,uploading the image of that vehicle, and reviewing a policycorresponding to that vehicle that has been generated by a neuralnetwork model analyzing the image, wherein the neural network model hasbeen trained on historical claim data and/or images of similar vehicles,according to at least one preferred embodiment.

Although the present invention has been described in considerable detailwith reference to certain preferred versions thereof, other versions arepossible, which may include additional or fewer features. For example,additional knowledge may be obtained using identical methods. Thelabeling techniques described herein may be used in the identificationof fraudulent claim activity. The techniques may be used in conjunctionwith co-insurance to determine the relative risk of pools of customers.External customer features, such as payment histories, may be taken intoaccount in pricing risk. Therefore, the spirit and scope of the appendedclaims should not be limited to the description of the preferredversions described herein.

Machine Learning & Other Matters

The computer-implemented methods discussed herein may includeadditional, less, or alternate actions, including those discussedelsewhere herein. The methods may be implemented via one or more localor remote processors, transceivers, servers, and/or sensors (such asprocessors, transceivers, servers, and/or sensors mounted on drones,vehicles or mobile devices, or associated with smart infrastructure orremote servers), and/or via computer-executable instructions stored onnon-transitory computer-readable media or medium.

Additionally, the computer systems discussed herein may includeadditional, less, or alternate functionality, including that discussedelsewhere herein. The computer systems discussed herein may include orbe implemented via computer-executable instructions stored onnon-transitory computer-readable media or medium.

A processor or a processing element may be trained using supervised orunsupervised machine learning, and the machine learning program mayemploy a neural network, which may be a convolutional neural network, adeep learning neural network, or a combined learning module or programthat learns in two or more fields or areas of interest. Machine learningmay involve identifying and recognizing patterns in existing data inorder to facilitate making predictions for subsequent data. Forinstance, machine learning may involve identifying and recognizingpatterns in existing text or voice/speech data in order to facilitatemaking predictions for subsequent data. Voice recognition and/or wordrecognition techniques may also be used. Models may be created basedupon example inputs in order to make valid and reliable predictions fornovel inputs.

Additionally or alternatively, the machine learning programs may betrained by inputting sample data sets or certain data into the programs,such as drone, autonomous or semi-autonomous drone, image, mobiledevice, vehicle telematics, smart or autonomous vehicle, and/orintelligent home telematics data. The machine learning programs mayutilize deep learning algorithms that may be primarily focused onpattern recognition, and may be trained after processing multipleexamples. The machine learning programs may include Bayesian programlearning (BPL), voice recognition and synthesis, image or objectrecognition, optical character recognition, and/or natural languageprocessing—either individually or in combination. The machine learningprograms may also include natural language processing, semanticanalysis, automatic reasoning, and/or machine learning.

In supervised machine learning, a processing element may be providedwith example inputs and their associated outputs, and may seek todiscover a general rule that maps inputs to outputs, so that whensubsequent novel inputs are provided the processing element may, basedupon the discovered rule, accurately predict the correct output. Inunsupervised machine learning, the processing element may be required tofind its own structure in unlabeled example inputs.

EXEMPLARY EMBODIMENTS

In one aspect, a computer-implemented method of detecting and/orestimating damage may be provided. The method may include (1) receiving,via one or more processors and/or associated transceivers (such as viawireless communication or data transmission over one or more radio linksor communication channels), free form text or free form speechassociated with a submitted insurance claim or a damage insured asset(such as home or vehicle), for instance the free form text or free formspeech may be associated with, or input via, a webpage accessed by acustomer or insurance agent, or an intranet page accessed by a callcenter representative; (2) identifying, via one or more processors, oneor more key words within the free form text or free form speech; and/or(3) based upon the one or more keywords, determining, via one or moreprocessors, a cause of loss and/or peril that caused damage to thedamaged insured asset to facilitate handling insurance claims andenhancing the online customer experience. The method may includeadditional, less, or alternate actions, including those discussedelsewhere herein.

For instance, the damaged insured asset may be a home, and the cause ofloss and/or peril may be wind, water, storm surge, smoke, fire, hail,hurricane, or tornado. The damaged insured asset may an autonomous orsemi-autonomous vehicle, and the cause of loss may be related to acollision or comprehensive (non-vehicle collision) cause of loss. Forvehicles, the cause of loss may include animals, such as deer, and thedamage may relate to one or more damaged or worn out sensors or otherelectronic components.

Identifying, via one or more processors, one or more key words withinthe free form text may include inputting the free form text or free formtext into a processor having a machine learning algorithm trained acceptthe free form text or free form speech and/or type of insured asset asinput, and then identify key words associated with cause of loss and/orinsurance perils. The machine learning algorithm may be dynamically orcontinuously updated or trained to dynamically update the key wordsassociated with cause of loss and/or insurance perils.

Determining, via one or more processors, a cause of loss and/or perilthat caused damage based upon the one or more key words may includeinputting the free form text or free form speech into a processor havinga machine learning algorithm trained to accept one or more key wordsand/or type of insured asset as input, and then identify a cause of lossand/or peril based upon the one or more key words and/or type of insuredasset. The machine learning algorithm may be dynamically or continuouslyupdated or trained to dynamically update the causes of loss and/orperils.

The method may include receiving, via one or more processors and/ortransceivers (such as via wireless communication or data transmissionover one or more radio links or communication channel), images of thedamaged insured asset (such as images submitted by the insured via awebpage); analyzing, via one or more processors, the images of thedamaged insured asset to determine a second cause of loss and/or secondperil; and/or comparing, via one or more processors, the second cause ofloss and/or second peril with the cause of loss and/or peril associatedwith the submitted insurance claim, respectively, to verify the accuracyof the submitted insurance claim, or identify potential fraud orbuildup.

The method may include receiving, via one or more processors and/ortransceivers (such as via wireless communication or data transmissionover one or more radio links or communication channel), images of thedamaged insured asset (such as images submitted by the insured via awebpage); and/or analyzing, via one or more processors, the images ofthe damaged insured asset to estimate damages and/or a repair orreplacement cost.

Analyzing, via one or more processors, the images of the damaged insuredasset to estimate damages and/or a repair or replacement cost for theinsured asset may include inputting the images into a processor having amachine learning algorithm trained to accept the images of a damageinsured asset as input, and estimate damages and/or repair/replacementcost for the insured asset.

The method may include retrieving or receiving, via one or moreprocessors, an insurance policy associated with the insured asset;and/or determining, via one or more processors, whether the cause ofloss and/or peril is covered under the insurance policy.

The damaged insured asset may be a vehicle, such as a smart orautonomous vehicle, and the cause of loss and/or peril may be, or may beassociated with, collision, comprehensive, bodily injury, propertydamage, liability, or medical. Additionally or alternatively, thedamaged insured asset may be a vehicle, such as a smart or autonomousvehicle, and the one or more key words may be, or may be associated withcollision, comprehensive, bodily injury, property damage, liability,medical, rental, towing, or ambulance.

The damaged insured asset may be a home or vehicle, and the one or morekey words may be, or may be associated with, fire, smoke, wind, hail,water, storm surge, tornado, hurricane, electrical, plumping, propertydamage, liability, medical, ambulance, materials, cabinets, fireplace,bathroom, bedroom, kitchen, upstairs, roof, downstairs, basement,structural security system, appliance, refrigerator, washer, dryer,oven, stove, and/or lightning.

In another aspect, a computer-implemented method of determining damageto property may be provided. The method may include (1) inputting, viaone or more processors, historical claim data into a machine learningalgorithm to train the algorithm to identify an insured asset (or typethereof), insured asset features or characteristics, a peril, and/or arepair or replacement cost; (2) receiving, via one or more processorsand/or transceivers (such as via wireless communication or datatransmission over one or more radio links or communication channel),images of the damaged insured asset (such as images submitted by theinsured via a webpage); and/or (3) inputting, via one or moreprocessors, the images of the damaged insured asset into a processorhaving the trained machine learning algorithm installed in a memoryunit, the trained machine learning algorithm identifying a type of thedamaged insured asset, features or characteristics of the damagedinsured asset, a peril, and/or a repair or replacement cost tofacilitate handling an insurance claim associated with the damagedinsured asset. The method may include additional, less, or alternateactions, including those discussed elsewhere herein.

For instance, the damaged insured asset may be a vehicle, and thefeatures or characteristics of the damaged insured asset include one ormore autonomous or semi-autonomous technologies or systems. Additionallyor alternatively, the damaged insured asset may be a vehicle, and thefeatures or characteristics of the damaged insured asset include one ormore autonomous or semi-autonomous technologies or systems, and/or theperil is collision, comprehensive, fire, or water.

The method may include retrieving, via one or more processors, aninsurance policy associated with the damaged insured asset; and/ordetermining, via one or more processors, whether the peril is a coveredperil under the insurance policy.

In another aspect, a computer system configured to detect and/orestimate damage may be provided. The system may include one or moreprocessors, sensors, transceivers, and/or servers configured to: (1)receive (such as via wireless communication or data transmission overone or more radio links or communication channels) free form text orfree form speech associated with a submitted insurance claim or a damageinsured asset (such as home or vehicle, which may be an autonomousvehicle), for instance the free form text or free form speech may beassociated with a webpage or website accessed by a customer or insuranceagent, or an intranet page accessed by a call center representative; (2)identify one or more key words within the free form text or free formspeech; and/or (3) based upon the one or more keywords, determine acause of loss and/or peril that caused damage to the damaged insuredasset to facilitate handling insurance claims and enhancing the onlinecustomer experience. The computer system may include additional, less,or alternative functionality, including that discussed elsewhere herein.

For instance, the system is further configured to: receive (such as viawireless communication or data transmission over one or more radio linksor communication channel), images of the damaged insured asset (such asimages submitted by the insured via a webpage); analyze the images ofthe damaged insured asset to determine a second cause of loss and/orsecond peril; and/or compare the second cause of loss and/or secondperil with the cause of loss and/or peril associated with the submittedinsurance claim, respectively, to verify the accuracy of the submittedinsurance claim, or identify potential fraud or buildup.

The system may be further configured to: receive (such as via wirelesscommunication or data transmission over one or more radio links orcommunication channel) images of the damaged insured asset (such asimages submitted by the insured via a webpage, website, and/or mobiledevice); and/or analyze the images of the damaged insured asset toestimate damages and/or a repair or replacement cost.

In another aspect, a computer system configured to determine damage toproperty may be provided. The system may include one or more processors,servers, sensors, and/or transceivers configured to: (1) inputhistorical claim data into a machine learning algorithm to train thealgorithm to identify an insured asset (or type thereof), insured assetfeatures or characteristics, a peril, and/or a repair or replacementcost; (2) receive (such as via wireless communication or datatransmission over one or more radio links or communication channel),images of the damaged insured asset (such as images submitted by theinsured via a webpage); and/or (3) input the images of the damagedinsured asset into a processor having the trained machine learningalgorithm installed in a memory unit, the trained machine learningalgorithm identifying a type of the damaged insured asset, features orcharacteristics of the damaged insured asset, a peril, and/or a repairor replacement cost to facilitate handling an insurance claim associatedwith the damaged insured asset. The system may include additional, less,or alternative functionality, including that discussed elsewhere herein.

In another aspect, a computer system configured to determine damage toproperty may be provided. The system may include one or more processors,servers, sensors, and/or transceivers configured to: (1) inputhistorical claim data into a machine learning algorithm to train thealgorithm to develop a risk profile for an insurable asset based upontype of insurable asset and insurable asset features or characteristics;(2) receive (such as via wireless communication or data transmissionover one or more radio links or communication channel), images of anundamaged insurable asset (such as images submitted by the insured via awebpage); and/or (3) input the images of the undamaged insurable assetinto a processor having the trained machine learning algorithm installedin a memory unit, the trained machine learning algorithm identifying ordetermining a risk profile for the insurable asset to facilitategenerating an insurance quote for the insurable asset. The system mayinclude additional, less, or alternative functionality, including thatdiscussed elsewhere herein.

The insurable asset may be a home, and the features or characteristicsmay include location, square footage, cabinet type, roof type, sidingtype, type of fireplace, type of windows, and/or material type, and/orother home features or characteristics.

The insurable asset may be a vehicle, and the features orcharacteristics include geographical area, make, model, transmission,tire, engine, autonomous or semi-autonomous features, types of sensorsor electronic components, versions of software (such as softwaredirecting or controlling autonomous or semi-autonomous features ortechnologies), air conditioning, power brakes, power windows, and/orcolor of the vehicle, and/or other vehicle features or characteristics.

The system may be configured to generate an insurance policy and/ordetermine an insurance rate, including a usage-based insurance (UBI)rate, for the insurable asset based at least in part upon the riskprofile developed for the insurable asset; and/or transmit the insurancepolicy and/or insurance rate to an asset owner for review and/orapproval.

In another aspect, a computer-implemented method for determining damageto property may be provided. The method may include, via one or moreprocessors, servers, sensors, and/or transceivers configured to: (1)inputting historical claim data into a machine learning algorithm totrain the algorithm to develop a risk profile for an insurable assetbased upon type of insurable asset and insurable asset features orcharacteristics; (2) receiving (such as via wireless communication ordata transmission over one or more radio links or communication channel)images of an undamaged insurable asset (such as images submitted by theinsured via a webpage); and/or (3) inputting the images of the undamagedinsurable asset into a processor having the trained machine learningalgorithm installed in a memory unit, the trained machine learningalgorithm identifying or determining a risk profile for the insurableasset to facilitate generating an insurance quote for the insurableasset. The method may include additional, less, or alternate actions,including those discussed elsewhere herein.

Additional Considerations

With the foregoing, any users (e.g., insurance customers)whose data isbeing collected and/or utilized may first opt-in to a rewards, insurancediscount, or other type of program. After the user provides theiraffirmative consent, data may be collected from the user's device (e.g.,mobile device, smart or autonomous vehicle controller, smart homecontroller, or other smart devices). In return, the user may be entitledinsurance cost savings, including insurance discounts for auto,homeowners, mobile, renters, personal articles, and/or other types ofinsurance. In the above description, neural networks may also refer toother methods of artificial intelligence and machine learning.

In other embodiments, deployment and use of neural network models at auser device (e.g., the client 202 of FIG. 2) may have the benefit ofremoving any concerns of privacy or anonymity, by removing the need tosend any personal or private data to a remote server (e.g., the server204 of FIG. 2).

The following additional considerations apply to the foregoingdiscussion. Throughout this specification, plural instances mayimplement operations or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. These and othervariations, modifications, additions, and improvements fall within thescope of the subject matter herein.

The patent claims at the end of this patent application are not intendedto be construed under 35 U.S.C. § 112(i) unless traditionalmeans-plus-function language is expressly recited, such as “means for”or “step for” language being explicitly recited in the claim(s). Thesystems and methods described herein are directed to an improvement tocomputer functionality, and improve the functioning of conventionalcomputers.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the description. Thisdescription, and the claims that follow, should be read to include oneor at least one and the singular also includes the plural unless it isobvious that it is meant otherwise.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Additionally, certain embodiments are described herein as includinglogic or a number of routines, subroutines, applications, orinstructions. These may constitute either software (e.g., code embodiedon a machine-readable medium) or hardware. In hardware, the routines,etc., are tangible units capable of performing certain operations andmay be configured or arranged in a certain manner. In exampleembodiments, one or more computer systems (e.g., a standalone, client orserver computer system) or one or more hardware modules of a computersystem (e.g., a processor or a group of processors) may be configured bysoftware (e.g., an application or application portion) as a hardwaremodule that operates to perform certain operations as described herein.

In various embodiments, a hardware module may be implementedmechanically or electronically. For example, a hardware module maycomprise dedicated circuitry or logic that is permanently configured(e.g., as a special-purpose processor, such as a field programmable gatearray (FPGA) or an application-specific integrated circuit (ASIC) toperform certain operations. A hardware module may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that istemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations.

Accordingly, the term “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. Considering embodiments inwhich hardware modules are temporarily configured (e.g., programmed),each of the hardware modules need not be configured or instantiated atany one instance in time. For example, where the hardware modulescomprise a general-purpose processor configured using software, thegeneral-purpose processor may be configured as respective differenthardware modules at different times. Software may accordingly configurea processor, for example, to constitute a particular hardware module atone instance of time and to constitute a different hardware module at adifferent instance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multipleof such hardware modules exist contemporaneously, communications may beachieved through signal transmission (e.g., over appropriate circuitsand buses) that connect the hardware modules. In embodiments in whichmultiple hardware modules are configured or instantiated at differenttimes, communications between such hardware modules may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware modules have access. Forexample, one hardware module may perform an operation and store theoutput of that operation in a memory product to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory product to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput products, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processor-implemented. For example, at least some of theoperations of a method may be performed by one or more processors orprocessor-implemented hardware modules. The performance of certain ofthe operations may be distributed among the one or more processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors maybe located in a single location (e.g., within a building environment, anoffice environment or as a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a buildingenvironment, an office environment, or a server farm). In other exampleembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

Some embodiments may be described using the expression “coupled” and“connected” along with their derivatives. For example, some embodimentsmay be described using the term “coupled” to indicate that two or moreelements are in direct physical or electrical contact. The term“coupled,” however, may also mean that two or more elements are not indirect contact with each other, but yet still co-operate or interactwith each other. The embodiments are not limited in this context,

Upon reading this disclosure, those of skill in the art will appreciatestill additional alternative structural and functional designs for asystem and a process of performing the methods and systems disclosedherein, using the principles disclosed herein. Thus, while particularembodiments and applications have been illustrated and described, it isto be understood that the disclosed embodiments are not limited to theprecise construction and components disclosed herein. Variousmodifications, changes and variations, which will be apparent to thoseskilled in the art, may be made in the arrangement, operation anddetails of the method and apparatus disclosed herein without departingfrom the spirit and scope defined in the appended claims.

1. A computer-implemented method of determining damage to personalproperty, the method comprising: inputting, via one or more processors,historical claim data into a machine learning algorithm to train thealgorithm to identify an insured vehicle, a respective type of theinsured vehicle, respective insured vehicle features or characteristics,a peril associated with the insured vehicle, and/or a repair orreplacement cost associated with the insured vehicle; receiving, via theone or more processors and/or the one or more transceivers, a digitalimage depicting damage to the insured vehicle, the digital imagesubmitted by an insured entity via a webpage, website, and/or mobiledevice; and inputting, via the one or more processors, the digital imageof the damaged insured vehicle into a processor having the trainedmachine learning algorithm installed in a memory unit, the trainedmachine learning algorithm identifying a type of the damaged insuredvehicle, a respective feature or characteristic of the damaged insuredvehicle, a peril associated with the damaged insured vehicle, and/or arepair or replacement cost associated with the damaged insured vehicleto facilitate handling an insurance claim associated with the damagedinsured vehicle or enhancing an online customer experience.
 2. Thecomputer-implemented method of claim 1, wherein the respective featuresor characteristics of the damaged insured vehicle include one or moreautonomous or semi-autonomous technologies or systems.
 3. Thecomputer-implemented method of claim 2, wherein the peril associatedwith the damaged insured vehicle comprises collision, comprehensive, theor water.
 4. The computer-implemented method of claim 1, the methodfurther comprising: retrieving, via the one or more processors and/orthe one or more transceivers, an insurance policy associated with thedamaged insured vehicle; and determining, via the one or moreprocessors, whether or not the peril associated with the damaged insuredvehicle is a covered peril under the insurance policy.
 5. Thecomputer-implemented method of claim 1, wherein the peril associatedwith the damaged insured vehicle comprises fire, smoke, water, hail,wind, or storm surge. 6.-17. (canceled)
 18. A non-transitory computerreadable medium containing program instructions that when executed,cause a computer to: input historical claim data into a machine learningalgorithm to operate the algorithm to identify a damaged insuredvehicle, respective damaged insured vehicle type, respective damagedinsured vehicle features or characteristics, a peril associated with thedamaged insured vehicle, and/or a repair or replacement cost associatedwith the damaged insured vehicle; receive a digital image depictingdamage to the insured vehicle, the digital image being submitted by aninsured entity via a webpage, webpage, or mobile device: and input theimage of the damaged insured vehicle into a processor having the trainedmachine learning algorithm installed in a memory unit, the trainedmachine learning algorithm identifying a type of the damaged insuredvehicle, a feature or characteristic of the damaged insured vehicle, aperil associated with the damaged insured vehicle, and/or a repair orreplacement cost associated with the damaged insured vehicle tofacilitate handling an insurance claim associated with the damagedinsured vehicle.
 19. The non-transitory computer readable medium ofclaim 18, wherein the features or characteristics of the damaged insuredvehicle include geographical area, make, model, transmission, tire,engine, autonomous or semi-autonomous features, air conditioning, powerbrakes, power windows, and/or color of the vehicle.
 20. Thenon-transitory computer readable medium of claim 18, containing furtherprogram instructions that when executed, cause the computer to: retrievean insurance policy associated with the damaged insured vehicle; anddetermine whether or not the peril associated with the damaged insuredvehicle is a covered peril under the insurance policy.